Coding of a digital audio signal, such as a speech signal, is commonly based on the use of a signal model to reduce bit rate (also called “rate” in the following) and maintain high signal quality. The use of a signal model enables the transformation of data to new data that are more amenable to coding or the definition of a distribution of the digital audio signal, which distribution can be used in coding. In a first example, the signal model may be used for linear prediction, which removes dependencies among samples of the digital audio signal (a method called linear predictive encoding). In a second example, the signal model may be used to provide a probability distribution of a signal segment of the digital audio signal to a quantizer, thereby facilitating the computation of the quantizer which operates either directly on the signal or on a unitary transform of the signal (method called adaptive encoding).
Delay is an important factor in many applications of coding of audio signals. In certain applications, for example those where the user receives an audio signal both through an acoustic path and through a communication-network path, the delay is particularly critical. To limit the delay associated with standard model estimation and transmission methods in such applications, it is common to use backward signal analysis (backward adaptive encoding), in which the model is extracted from previously quantized segments of the digital audio signal (called signal reconstruction in the following).
Coding methods are commonly divided into two classes, namely variable-rate coding, which corresponds to constrained-entropy quantization, and fixed-rate coding, which corresponds to constrained-resolution quantization. The behaviour of these two coding methods can be analysed for the so-called high-rate case, which is often considered to be a good approximation of the low-rate case. A constrained-resolution quantizer minimizes the distortion under a fixed-rate constraint, which, at high rate, results generally in non-uniform cell sizes. In contrast, a constrained-entropy quantizer minimizes the distortion under an average rate (the quantization index entropy) constraint. Thus, in this latter case, the instant rate varies over time, which, at high-rate, generally results in an uncountable set of quantization cells of uniform size and shape while redundancy removal is left to lossless coding.
An advantage of constrained-entropy quantization over constrained-resolution quantization is that it provides a (nearly) constant distortion, which is especially beneficial when the signal model or probabilistic signal model is not optimal. However, a non-optimal probabilistic signal model leads also to an increase in bit rate in the case of constrained-entropy coding. In contrast, constrained-resolution quantization leads to an increased distortion while keeping a constant rate when the probabilistic signal model is not optimal.
Normally, speech and audio signals display so-called transitions, at which the optimal probabilistic signal model would change abruptly. If the model is not updated immediately at a transition, the quality of the encoding degrades in the constrained-resolution case (increased distortion) while the bit rate increases in the constrained-entropy case.
The problem at transitions is particularly significant when the probabilistic signal model is updated by a backward signal analysis. In the case of constrained-resolution quantization, the problem at transitions leads to error propagation since the signal reconstruction is inaccurate because the signal model is inaccurate, and the signal model is inaccurate because the signal reconstruction is inaccurate. Thus, it takes a relatively long time for the coder to retrieve a good signal quality. In the case of constrained-entropy quantization, there is little error propagation but the bit rate increases significantly at abrupt transitions (resulting in bit rate peaks).
Thus, there is a need for providing improved methods and devices for encoding and decoding audio signals, which methods and devices would overcome some of these problems.